
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "162"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 88 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 84 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.462616	-0.575708	2.144988	2.146621	1.671784	2.032737	1.950486	1.668977	0.8010	0.8076	0.2371	14.529861	14.992399
2459593	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.995056	-0.217804	2.457416	3.043220	3.090722	3.081756	0.633188	0.220834	0.7488	0.7502	0.2402	5.801366	5.909983
2459592	digital_maintenance	0.00%	100.00%	100.00%	0.00%	-	-	0.827987	-1.126844	1.081804	0.539293	0.769646	0.387186	2.357452	0.139761	0.0864	0.0406	0.0215	nan	nan
2459591	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.783345	0.148643	3.632494	3.172880	3.519428	2.750413	0.111509	0.893799	0.6857	0.7016	0.3631	5.366976	5.583682
2459590	digital_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.902970	0.380461	2.609867	2.728204	4.517224	3.134627	3.328543	0.933663	0.6840	0.6990	0.3575	5.250588	5.655344
2459589	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.878156	0.311250	1.590460	2.074710	0.947551	0.643869	1.178737	0.617419	0.7244	0.7242	0.2754	5.726531	5.799225
2459588	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.951000	-0.415173	3.316923	3.557931	2.367661	2.728576	0.962810	0.709530	0.7906	0.7781	0.2113	9.789174	9.033145
2459587	digital_maintenance	100.00%	-	-	-	100.00%	0.00%	348.265286	357.140440	inf	inf	26.558341	108.315257	199.223957	556.220096	nan	nan	nan	0.000000	0.000000
2459586	digital_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	306.785514	318.854034	inf	inf	10.251396	15.670716	189.342616	745.781222	nan	nan	nan	0.000000	0.000000
2459585	digital_maintenance	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.860369	-1.281806	0.194053	-0.032214	-0.510740	-0.403037	1.691587	2.241279	0.0914	0.0426	0.0173	0.000000	0.000000
2459584	digital_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	736.782516	1369.361995	inf	inf	2955.326168	4750.114802	21142.250946	34082.833031	nan	nan	nan	0.000000	0.000000
2459583	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.370161	0.221532	-0.552284	-0.875241	2.739605	1.014709	-0.516033	0.475203	0.6891	0.7010	0.3748	4.569368	4.532090
2459582	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.854428	0.058767	-0.672998	-0.725635	3.759341	1.449581	-0.614004	0.314610	0.6898	0.7013	0.3777	3.639183	3.585122
2459581	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.318833	0.350655	-0.727754	-0.845010	3.080065	0.802407	-0.297744	0.293358	0.6979	0.7071	0.3734	3.748874	3.634918
2459580	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.390256	-0.156700	-0.814320	-0.727247	2.444855	1.083164	-0.657438	0.227136	0.6898	0.6996	0.3731	4.751274	4.373187
2459579	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.670580	0.218618	-0.676460	-0.723821	3.328020	1.305982	-0.279683	1.119781	0.6861	0.6956	0.3733	0.000000	0.000000
2459578	digital_maintenance	0.00%	100.00%	100.00%	0.00%	-	-	-0.342898	0.213822	-0.644468	-0.492226	0.697886	0.798800	-0.008223	0.708253	0.1003	0.0736	0.0240	nan	nan
2459577	digital_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459576	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.055589	0.316822	-0.613498	-0.727210	2.436482	1.425748	-0.150693	0.064715	0.7111	0.7213	0.3741	6.739906	6.139468
2459575	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.592435	0.331080	-0.632260	-0.684187	2.084016	0.727773	-0.610094	-0.680124	0.7701	0.7735	0.3138	5.378209	5.165818
2459574	digital_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459573	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.435050	-0.372789	-0.589992	-0.936530	3.613955	0.720063	1.244856	0.610404	0.7100	0.7232	0.3708	5.099599	4.882811
2459572	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.644933	-0.356852	-0.672033	-0.811919	2.227567	1.126344	0.947192	0.696390	0.7118	0.7256	0.3746	5.851711	5.915348
2459571	digital_maintenance	100.00%	100.00%	100.00%	0.00%	-	-	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	nan	nan
2459570	digital_maintenance	100.00%	0.00%	29.19%	0.00%	100.00%	0.00%	-0.344484	-0.265220	-0.321282	-0.564315	-0.648222	55.949122	-0.744407	1.536065	0.6964	0.5618	0.3827	14.112469	15.309323
2459569	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.715387	-0.304408	-0.700633	-0.907737	-0.339434	-0.470139	-0.544708	-0.853829	0.7816	0.7886	0.2237	4.711517	4.588823
2459566	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.642458	-0.544645	-0.565797	-0.848735	2.559988	1.097632	-0.268310	1.238472	0.7008	0.7153	0.3900	3.685486	3.531520
2459565	digital_maintenance	0.00%	-	-	-	100.00%	0.00%	-0.000390	-0.000390	0.000000	0.000000	-0.823769	-0.823769	-0.908427	-0.908427	nan	nan	nan	0.000000	0.000000
2459564	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.854938	-0.262140	-0.772443	-0.838884	1.148850	0.463362	-0.132946	0.426007	0.7210	0.7233	0.3831	3.999084	3.570880
2459563	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.149444	-0.521755	-0.533540	-0.850619	2.778989	0.824397	1.206923	1.489086	0.7153	0.7238	0.3752	4.266770	4.256831
2459562	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.508560	-0.261778	-0.187671	-0.218209	1.557178	0.969578	0.672426	0.376545	0.7186	0.7283	0.3718	4.042097	3.551574
2459561	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.054083	-0.221073	-0.132237	-0.258588	1.513691	1.095299	0.012587	0.096371	0.7138	0.7273	0.3733	3.447470	3.194869
2459560	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.634684	-0.223254	-0.009219	-0.321370	2.119779	1.330808	0.000977	0.235106	0.7144	0.7246	0.3846	3.868656	3.363742
2459559	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.538734	-0.400109	-0.307436	-0.529711	2.047690	1.209140	0.121710	0.131268	0.7121	0.7202	0.3763	3.451018	3.100288
2459558	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.913126	-0.459657	-0.680030	-0.700104	2.692005	0.992750	-0.515413	0.368314	0.7185	0.7230	0.3766	4.552339	3.904418
2459557	digital_maintenance	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.1247	0.0451	0.0319	nan	nan
2459556	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.092360	-0.331846	-0.754940	-0.666976	3.031785	1.420062	-0.334244	0.917587	0.7107	0.7185	0.3789	4.265733	3.852753
2459554	digital_maintenance	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7205	0.7281	0.3867	nan	nan
2459553	digital_maintenance	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7154	0.7226	0.3870	nan	nan
2459552	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.424959	0.596104	-0.669431	-0.295673	2.482208	0.675581	0.780776	1.373804	0.7211	0.7262	0.3811	9.079129	10.007969
2459551	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.456898	-0.448519	-0.366011	-0.528687	2.518708	1.448694	-0.036418	0.220541	0.7187	0.7250	0.3837	3.862169	3.424669
2459550	digital_maintenance	-	99.18%	99.18%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0801	0.0432	0.0121	nan	nan
2459549	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.792509	-0.526702	-0.814971	-0.933304	3.735802	0.840820	-0.451130	0.943186	0.7131	0.7190	0.3939	3.897868	3.435430
2459542	digital_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459541	digital_maintenance	0.00%	1.34%	1.79%	0.00%	100.00%	0.00%	2.733757	-0.087360	-0.601403	-0.782295	-0.322728	-1.166272	2.241196	0.375456	0.7549	0.7499	0.3195	4.987418	4.998525
2459540	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.229940	0.201260	-0.135749	-0.183220	2.239005	-0.543685	0.141809	0.763031	0.7644	0.7646	0.3320	3.366524	2.969606
2459536	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.759904	0.389980	-0.331405	-0.115179	0.728779	0.445885	-0.662700	0.368731	0.7476	0.7494	0.4040	4.540252	3.803062
2459535	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.810946	-0.156385	0.058395	-0.190595	1.115976	-0.972870	-0.165352	0.664780	0.7949	0.7877	0.3392	3.687861	3.133368
2459534	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.179385	-0.331270	0.259532	-0.131370	1.404516	-0.251601	0.151523	1.299589	0.7812	0.7671	0.3528	4.819722	3.766743
2459533	digital_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459532	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.203333	0.001941	-0.645045	-0.877256	3.865072	0.523549	2.016622	2.249406	0.7403	0.7464	0.3822	4.823184	4.051318
2459530	digital_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.305006	-0.339327	-0.514722	-0.393661	3.096969	1.247604	11.807204	4.333685	0.7479	0.7510	0.3767	3.738161	3.460034
2459527	digital_maintenance	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.789107	-0.039986	-0.095984	-0.923790	0.172535	0.209592	0.196670	1.661349	0.9125	0.9396	0.2823	0.000000	0.000000
2459523	digital_maintenance	0.00%	-	-	-	100.00%	0.00%	1.701260	-0.244992	-0.574180	-1.075694	0.697890	-0.435862	-0.220304	0.634114	nan	nan	nan	6.280108	5.122544
2459522	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.846527	1.157869	-0.831954	-0.847091	-0.542587	-0.375395	-0.459092	2.237326	0.7058	0.7003	0.3196	4.944781	5.856686
2459513	digital_maintenance	0.00%	100.00%	100.00%	0.00%	-	-	-0.048697	0.161745	-0.554359	-0.781795	0.205487	0.225335	-0.113779	2.069012	0.0842	0.0715	0.0011	nan	nan
2459508	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.192531	-0.456562	-0.186502	-0.542385	1.188476	-0.158682	-0.182082	-0.813789	0.9222	0.9474	0.1612	0.000000	0.000000
2459505	digital_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.532317	-0.419396	-0.456437	-0.903464	1.263966	-0.649430	7.315757	2.313618	0.8891	0.8587	0.1962	31.640136	25.884855
2459503	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.405466	-0.721471	-0.848152	-0.882321	1.321911	0.981369	1.313231	1.924983	0.8872	0.8813	0.4107	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 162 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 162, Node 13:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 162: 2459612

Antenna 162: 2459611

Antenna 162: 2459610

Antenna 162: 2459609

Antenna 162: 2459608

Antenna 162: 2459607

Antenna 162: 2459605

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Antenna 162: 2459588

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Antenna 162: 2459583

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Antenna 162: 2459579

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Antenna 162: 2459577

Antenna 162: 2459576

Antenna 162: 2459575

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Antenna 162: 2459571

Antenna 162: 2459570

Antenna 162: 2459569

Antenna 162: 2459566

Antenna 162: 2459565

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Antenna 162: 2459563

Antenna 162: 2459562

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Antenna 162: 2459556

Antenna 162: 2459552

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Antenna 162: 2459542

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Antenna 162: 2459536

Antenna 162: 2459535

Antenna 162: 2459534

Antenna 162: 2459533

Antenna 162: 2459532

Antenna 162: 2459530

Antenna 162: 2459527

Antenna 162: 2459524

Antenna 162: 2459523

Antenna 162: 2459522

Antenna 162: 2459513

Antenna 162: 2459508

Antenna 162: 2459506

Antenna 162: 2459505

Antenna 162: 2459503

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶